Breaking the Single Voice: Perceived AI Plurality as an Autonomy-Supportive Architecture for Ethical Marketplace Decisions

Beyond the Default: How Customizable Artificial Intelligence Agents Can Attenuate Stereotypical Preferences

AI agents are typically deployed with gender cues that match traditional role expectations, such as female voices or avatars in assistive and support roles, because stereotype‐congruent designs enhance consumer adoption, trust, and engagement. However, prior research suggests that this practice risks entrenching occupational gender stereotypes at a societal level, raising the question of whether, and under what conditions, consumers’ stereotype‐congruent preferences can be attenuated. Drawing on Role Congruity Theory and research on bias correction, we propose that preferences for gender‐stereotypical AI agents are not fixed but contingent on the salience of gender cues in the decision context: when gender is foregrounded as a potential source of bias, consumers may deliberately shift toward counterstereotypical choices. Four experimental studies ( N  = 2530) test this proposition. Study 1 establishes that, under low gender‐cue salience, consumers default to AI agents whose gender aligns with stereotypical occupational roles. Study 2 shows that making gender cues salient in the choice process can attenuate these default preferences, and, under certain conditions, shift choices toward counterstereotypical agents. Studies 3 and 4 provide process evidence consistent with a role for action‐efficacy beliefs, whereby individuals come to view choosing a counterstereotypical AI agent as a meaningful way to promote gender equality. Gender salience, whether induced through design choices (Study 3) or explicit bias warnings (Study 4), was associated with stronger action‐efficacy beliefs, and the conditional indirect effects through these beliefs emerged primarily among women in male‐typed contexts, while comparable effects were weaker or absent among men and in female‐typed domains. Our findings identify conditions under which AI design choices can attenuate stereotype‐congruent preferences and provide evidence regarding the psychological processes associated with these shifts, offering actionable insights for managers and policymakers seeking to design AI agents that support more equitable human–AI interactions.

Do AI-Personalised Advertisements Influence Decision Autonomy Among Young Consumers?

The Personalization Paradox: How AI-Driven Green Marketing Can Advance Sustainable Consumption Without Triggering Consumer Skepticism

This article synthesizes recent empirical evidence to demonstrate that personalization can significantly enhance sustainable consumption when implemented through transparent, reasoning-enabled AI systems that prioritize ethical data stewardship.

Towards Ethical AI-driven Marketing: A Framework for Explainability, Consumer Empowerment and Algorithmic Accountability in Emerging Markets

The diffusion of artificial intelligence into marketing practice has brought with it a fundamental governance problem: The most predictively capable AI systems are, typically, the least interpretable. This opacity creates information asymmetries between firms and consumers and complicates the ethical legitimacy of automated marketing decisions. This article argues that explainable AI (XAI) constitutes a necessary, and currently undertheorised, layer in the design of consumer-facing AI systems. Drawing on consumer trust theory, algorithmic decision-making research and marketing ethics literature, we develop the explainable marketing AI (XMAI) framework: a theoretical model proposing four interdependent components—algorithmic transparency, contextual relevance, consumer empowerment and ethical accountability—that jointly govern the deployment of explainable AI systems in marketing contexts. Four propositions link these components to consumer trust, perceived fairness, long-term engagement and consumer well-being. The framework is developed in the context of India and comparable developing economies, where rapid AI adoption in marketing is outpacing governance infrastructure, and where consumer digital literacy and institutional trust present conditions that differ from the Western settings in which most AI ethics frameworks have been formulated. The article contributes a theoretical foundation for researchers, practitioners and policymakers to close the gap between AI capability and accountability in marketing.

AI-Driven Personalization and Purchase Intention: The Mediating Role of Algorithmic Fairness Perception

Artificial intelligence (AI)-driven personalization has revolutionized digital commerce by enabling platforms to provide highly tailored recommendations, customized content, and adaptive shopping experiences. Although prior research has established that AI personalization positively influences consumer responses through dimensions such as trust, usefulness, and satisfaction, there has been limited exploration of how consumers assess the fairness of algorithmic decision-making prior to forming purchase intentions. Furthermore, few studies have integrated AI-driven personalization, brand experience, perceived value, and social proof into a cohesive framework that elucidates consumer behavior through the lens of algorithmic fairness perception. To address this gap, the present study examines the mediating role of algorithmic fairness perception in elucidating how AI-enabled marketing stimuli affect purchase intention. A quantitative cross-sectional survey was conducted involving 400 consumers with experience using AI-enabled e-commerce platforms, and the proposed model was analyzed utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that AI-driven personalization, brand experience, perceived value, and social proof significantly enhance algorithmic fairness perception. Additionally, AI-driven personalization, brand experience, perceived value, and algorithmic fairness perception substantially increase purchase intention, while social proof does not exhibit a significant direct effect. Nevertheless, algorithmic fairness perception serves as a significant mediator in the relationships between all antecedent variables and purchase intention. The originality of this study lies in positioning algorithmic fairness perception as the central psychological mechanism that translates AI-enabled marketing strategies into consumer purchase intention within a Stimulus–Organism–Response (SOR) framework. These findings contribute to the growing body of literature on AI marketing by shifting the analytical focus from technological effectiveness to ethical algorithmic evaluation, offering practical guidance for the development of transparent, responsible, and consumer-oriented AI systems that promote sustainable customer relationships.

Autonomy Deprivation, Villain Appeal, and Aspirational Brand Identification in Overchoice Environments

This study investigates whether autonomy deprivation resulting from overchoice environments predicts individuals' attraction to morally decisive fictional antagonists and unconventional brand personalities. Drawing on Self-Determination Theory, the Paradox of Choice, media psychology, and consumer behaviour literature, the study proposes that excessive choice reduces individuals' sense of autonomy, increasing their preference for characters and brands that project certainty and decisiveness. A mixed-methods approach was adopted, combining a thematic review of peer-reviewed literature with a small-scale anonymous survey conducted through Google Forms. The findings indicate that respondents who experienced greater choice overload also reported stronger attraction toward decisive fictional villains and brands with bold, distinctive identities. The results suggest that decisiveness may serve as a psychological substitute for diminished autonomy in environments characterised by excessive choice. The study contributes to the literature by integrating three previously disconnected research areas and proposes a unified framework linking decision environments, psychological needs, media preferences, and consumer behaviour. Although limited by a small sample size and self-reported data, the findings provide a foundation for future empirical research examining how autonomy influences both entertainment preferences and marketplace behaviour.